A graduation project developing an innovative solution for car recognition and intelligence using artificial intelligence (AI) technology.
CarVision is a graduation project that showcases the power of AI in automotive recognition. This mobile application provides instant car identification and price prediction capabilities, processing images in under 2 seconds. The project demonstrates practical applications of deep learning and mobile development technologies in solving real-world problems.
- 🎯 70% Test Accuracy across 899 car models
- 📊 1.45M Training Images from DVM-CAR 2.0 dataset
- ⚡ 40% Reduced Training Time through optimization and Transfer Learning
- 🚀 <2s Processing Speed for real-time results
- Instant Car Recognition: Upload or capture images for immediate classification
- Price Prediction: Get market value estimates based on historical data
- Cross-Platform Support: Built with Flutter for iOS and Android
- User-Friendly Interface: Intuitive design for seamless experience
- TensorFlow for deep learning
- Transfer learning optimization
- Custom data preprocessing pipeline
- Flutter framework
- Real-time camera integration
- Responsive UI design
- Node.js server
- MongoDB database
- RESTful API architecture
# Install Flutter
flutter doctor
# Install Node.js dependencies
cd nodejs
npm install
# Install Flutter dependencies
cd flutter
flutter pub get# Start backend server
cd nodejs
npm run dev
# Launch Flutter app
cd flutter
flutter runCarVisionProject/
├── nodejs/ # Node.js server and API
├── flutter/ # Flutter application
└── preprocessing/ # Data preprocessing scripts
- Classification Support: 899 car models
- Test Accuracy: 70%
- Processing Time: <2 seconds
- Dataset: DVM-CAR 2.0 (1.45M images)
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.
Taha Khamessi & Mohammed Louai Lamsi
- Graduation Project (2024)
- Computer Science
- Coursework: Software Engineering and Information Systems
Made with ❤️ as a graduation project by Taha Khamessi and Mohammed Louai Lamsi